code: def test_vectorassembler_converter(self):
      iris = load_iris()
      features = [
          "sepal_length",
          "sepal_width",
          "petal_length",
          "petal_width",
      ]
  
      pd_df = pd.DataFrame(
          data=np.c_[iris["data"], iris["target"]],
          columns=features + ["target"],
      )[["sepal_length", "sepal_width", "petal_length", "petal_width"]]
      df = sql.createDataFrame(pd_df)
  
      model = VectorAssembler(inputCols=features, outputCol="features")
  
      test_df = df
      torch_model = convert(model, "torch", test_df)
      self.assertTrue(torch_model is not None)
  
      spark_output = model.transform(test_df).toPandas()
      spark_output["features"] = spark_output["features"].map(
          lambda x: np.array(x.toArray())
      )
      spark_output_np = spark_output["features"].to_numpy()
      torch_output_np = torch_model.transform(pd_df)
  
      np.testing.assert_allclose(
          np.vstack(spark_output_np),
          torch_output_np,
          rtol=1e-06,
          atol=1e-06,
      )
  
file_path: hummingbird/tests/test_sparkml_vector_assembler.py
imports: import unittest
  import warnings
  import numpy
  import torch
  from sklearn.datasets import load_iris
  from packaging.version import Version, parse
  from hummingbird.ml._utils import sparkml_installed, pandas_installed
  from hummingbird.ml import convert
label_desc: Use image node_id 1 for calling the TestSparkMLVectorAssembler obj's underlying member method code with example usage: obj.test_vectorassembler_converter() without return types